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8th International Conference on Mechatronics engineering, ICOM 2022

作者： Alatabani, Lina E. Ali, E.S. Mokhtar, R.A. Khalifa, Othman O. Saeed, Rashid A. Department of Data Communications & Network Engineering Faculty of Telecommunications The Future University Khartoum Sudan port Sudan Sudan Khartoum Sudan Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Electrical and Electronics Engineering Faculty of Engineering International Islamic University Malaysia Malaysia The Libyan Center for Engineering Research and Information Technology Bani Walid Libya

ISBN: (数字)9781839538063

ISBN: (纸本)9781839538063

Robotics has been playing a vital role in our daily lives with a wide range of applications to improve the quality of life. With a variety of usable applications in the medical, manufacturing, and transportation industries, there is a continuous need for improving the performance of robotics for the importance of precision in executing commands and tasks. The implementation of precise commands has led to intense research on approaches to improve the performance of robotics. Machine Learning (ML) and Deep Learning (DL) have been drawing attention to applying architectures and algorithms to robotics which imposed a positive impact on the field of robotics. ML and DL applications in robotics include areas of computer vision, imitation learning, self-supervised learning, assistive and medical technologies, multi-agent learning, and manufacturing. This paper provides a comprehensive review of autonomous vs automatic robotics, robotic applications, extreme learning machine methods, and ML for soft robotics applications, in addition, to discussing the challenges, and future trends for AI applications in robotics applications. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Robotics

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Objective quality assessment of generated high-resolution human face images

Objective quality assessment of generated high-resolution hu...

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International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)

作者： Jiacheng Gu Ding Yue Shaode Yu Peng Zhang Qiurui Sun School of Computer Science Beijing Institute of Technology Beijing China School of Info. and Comm. Engineering Communication University of China Beijing China Education Management Information Center Ministry of Education of the PRC Beijing China Center of Information & Network Technology Beijing Normal University Beijing China

ISBN: (数字)9798331510633

ISBN: (纸本)9798331510640

Human face image generation can be used in media, marketing, education, creativity, research and privacy protection. Many deep learning networks have been proposed for generating high-resolution human face images, while the quality of generated human face images are not fully investigated. To bridge this gap, 11 StyleGAN-based networks are firstly trained via the database Flickr-Faces-HQ (FFHQ). Then, each network produces 10,000 high-resolution (1024 × 1024) human face images. After that, the quality of these generated images are evaluated with 6 Face Image Quality Assessment (FIQA) metrics. The average metric values suggest that the generated human face images are with higher quality than the realistic images in the FFHQ database. However, the failure cases of generated images remind us that artifacts and distortions exist, and unreasonable combinations and meaningless contents are produced. Therefore, significant progress is still required for achieving reliable quality assessment and high-accuracy generation of human faces.

关键词： Measurement Image quality Training Image synthesis Databases Face recognition Quality assessment Reliability Protection Faces

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Research on technology Against Deepfake

Research on Technology Against Deepfake

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Mobile networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Xia Wei Wenjun Zhang Guozhu Bai Bo Li School of Information Engineering Xi ‘an Mingde Institute of Technology Xi'an China Shaanxi Branch of National Computer Emergency Network Response Technical Team Coordination Center of China Xi'an China

With the help of social networks, Deepfakes enable false information to be presented to netizens in a highly credible manner, which will have a huge impact on individuals, organizations, and countries. It will also pose huge information security risks to individuals, organizations, and countries. Some countries are even seeking to weaponize “deep counterfeiting technology”, which poses significant security risks and has become a focus and difficulty of research both domestically and internationally. Scholars both domestically and internationally are exploring different deep forgery detection countermeasures to eliminate negative impacts. It was found that at the technical level, it can be divided into two types based on processing methods: passive detection algorithms and active defense technologies. According to the different processing objects, passive detection algorithms can be divided into two categories: deep forged image detection and deep forged video detection. In terms of active defense technology, content traceability technology is mainly used to build a trusted system for digital content in the internet to ensure the source security of content. This paper summarizes the current research situation of combating deepfake technology based on blockchain technology from three aspects of building a trusted network, tracing deepfake and content Tamper resistance prevention, analyzes the limitations and risks of using blockchain technology against deepfake technology, and discusses the future research direction.

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Load Balancing in Small-Cell Access Point Placement

Load Balancing in Small-Cell Access Point Placement

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IEEE Conference on Vehicular technology (VTC)

作者： Govind R. Gopal Bhaskar D. Rao Gabriel Porto Villardi Department of Electrical and Computer Engineering University of California San Diego La Jolla United States Wireless Network Research Center National Institute of Information and Communications Technology Yokosuka Japan

We address the uplink small-cell access point (AP) placement problem for optimal throughput, while considering load balancing (LB) among the APs. To consider LB and consequently incorporate fairness in user spectral access, i.e., the frequency of user-to-AP communications, we modify the Lloyd algorithm from vector quantization so that delays incurred by the existence of a large number of users in a cell are accounted for in the AP placement process. Accordingly, we present two methods, the first of which involves the incorporation of weights proportional to the cell occupancy, hence called the Occupancy Weighted Lloyd algorithm (OWLA). The second method adds a new step to the Lloyd algorithm, which involves re-assigning users from higher to lower occupancy cells, and the adoption of a distance threshold to cap the throughput lost in the assignment process. This formulated Lloyd-type algorithm is called the Cell Equalized Lloyd Algorithm-α (CELA-α) where α is a factor that allows for throughput and spectrum access delay trade-off. Extensive simulations show that both CELA-α and OWLA algorithms provide significant gains, in comparison to the standard Lloyd algorithm, in 95%-likely user spectral access. For the α values considered in this paper, CELA-α achieves gains up to 20.83%, while OWLA yields a gain of 12.5%. Both algorithms incur minimal throughput losses of different degrees, and the choice of using one algorithm over the other for AP placement depends on system LB as well as throughput requirements.

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Microservice Auto-Scaling Algorithm Based on Workload Prediction in Cloud-Edge Collaboration Environment

Microservice Auto-Scaling Algorithm Based on Workload Predic...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Zijun Peng Bing Tang Wei Xu Qing Yang Ehsanullah Hussaini Yuqiang Xiao Haiyan Li School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China Center of Network and Education Technology Guangzhou Maritime University Guangzhou China

Cloud computing centrally consolidates hardware and computing resources, offering efficient and cost-effective services. However, as cloud computing centers are predominantly built and operated in a fully centralized fashion, the increased distance between these centers and users can lead to a decline in service quality. Real-time interaction and high business continuity are crucial in scenarios like traffic monitoring, AR/VR applications, and the Internet of Things (IoT). Edge computing is better suited to meet the demands of such latency-sensitive business needs. By analyzing and processing massive data directly at edge computing nodes, which focus on network edge devices, reliance on transmission resources is reduced, consequently improving the overall quality and performance of services. Nevertheless, resource-constrained edge nodes require efficient utilization of available infrastructure capacity to ensure specific service level objectives (SLO) for applications. Therefore, this paper introduces XScale, a cloud-edge collaborative system that enables microservices to adaptively scale elastically. XScale applies a Bi-LSTM with an attention mechanism to forecast the workload of microservices. When combined with mechanisms designed to handle burst traffic and a cloud-edge collaborative load forwarding strategy, it achieves both adaptive elastic scaling and proactive load forwarding. Experimental results, obtained using real-world microservice workloads, indicate that the XScale system can significantly reduce SLO violations by 88%, increase resource utilization by 15%, and decrease average response time by 21% when compared to existing advanced reactive scaling methods.

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A Blockchain-based PHR Sharing Scheme with Attribute Privacy Protection

A Blockchain-based PHR Sharing Scheme with Attribute Privacy...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Chaohe Lu Zhongyuan Yu Guijuan Wang Anming Dong Xiang Tian School of Computer Science Qufu Normal University Rizhao China School of Information Science and Engineering Lanzhou University Lanzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

With the rapid advancement and application of the Internet of Medical Things (IoMT), personal health records (PHRs) are now increasingly comprised of data collected by Internet of Things (IoT) devices and medical records documented by healthcare professionals. Personal health record (PHR) sharing demonstrates great potential in improving the accuracy of disease diagnosis. However, PHR sharing also brings risks such as illegal access and personal information leakage. Some works explored using blockchain or attribute-based encryption (ABE) to solve these privacy leakage problems, but those solutions did not pay attention to the user’s attribute privacy. In this work, we combine a linear secret sharing scheme (LSSS) and zero-knowledge succinct non-interactive argument of knowledge (zkSNARK) scheme to design an efficient zero-knowledge proof protocol called zk-AHSNARK. It can verify the user’s attribute permissions while also hiding attribute information. Based on zk-AHSNARK, we propose a novel PHR sharing scheme that protects attribute privacy. Data security is ensured by storing encrypted data in the interplanetary file system (IPFS). In addition, we introduce keyword ciphertext search to achieve fast data retrieval, and we implement the search and verification algorithms via a smart contract, ensuring the trustworthiness and integrity of the execution. Finally, through a large number of simulations, we demonstrated the suggested scheme’s viability and security.

关键词： Access control Privacy Smart contracts Blockchains Encryption InterPlanetary File System Medical diagnosis Protection Secure storage Medical diagnostic imaging

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A Robust Semantic Communication System for Image Transmission

A Robust Semantic Communication System for Image Transmissio...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Xiang Peng Zhijin Qin Xiaoming Tao Jianhua Lu Khaled B. Letaief Department of Electronic Engineering Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology (BNRist) Beijing China State Key Laboratory of Space Network and Communications Beijing China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Semantic communications have gained significant attention as a promising approach to address the transmission bottleneck, especially with the continuous development of 6G techniques. Distinct from the well investigated physical channel impairments, this paper focuses on semantic impairments in images, particularly those arising from adversarial perturbations. Specifically, we propose a novel metric for quantifying the intensity of semantic impairment and develop a semantic impairment dataset. Furthermore, we introduce a deep learning enabled semantic communication system, termed as DeepSC-RI, to enhance the robustness of image transmission, which incorporates a multi-scale semantic extractor with a dual-branch architecture for extracting semantics with varying granularity, thereby improving the robustness of the system. The fine-grained branch incorporates a semantic importance evaluation module to identify and prioritize crucial semantics, while the coarse-grained branch adopts a hierarchical approach for capturing the robust semantics. These two streams of semantics are seamlessly integrated via an advanced cross-attention-based semantic fusion module. Experimental results demonstrate the superior performance of DeepSC-RI under various levels of semantic impairment intensity.

关键词： Measurement Image communication Perturbation methods Semantics Rician channels Semantic communication Transformers Robustness Streams Signal to noise ratio

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HPCNet: A Hybrid Progressive Coupled network for Image Deraining

HPCNet: A Hybrid Progressive Coupled Network for Image Derai...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Qiong Wang Kui Jiang Jinyi Lai Zheng Wang Jianhui Zhang National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering Huawei Technologies Cloud BU College of Computer Science and Technology Hangzhou Dianzi University

Convolutional neural networks (CNNs) and Transformers have significantly succeeded in low-level vision tasks. Although prominent complementary characteristics exist regarding the larger receptive field and better convergence, only some efforts have compacted them efficiently due to their individual and nonnegligible weakness. In this paper, we propose a hybrid progressive coupled network (HPCNet) for rain perturbation removal, which integrates the advantages of these two architectures while maintaining both effectiveness and efficiency. In particular, we achieve the progressive decomposition and association of rain-free and rain features, designed as an asymmetrical dual-path mutual representation network to alleviate the computational cost. Meanwhile, we equip the network with high-efficiency convolutions and resolution rescaling strategy to trade off the computational complexity. Extensive experiments show that our method outperforms MPRNet on average while saving 87.2% and 61.1% of the computational cost and parameters.

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An Ensemble Convolutional Neural network for Recognizing and Counting of Mango Pests using Superpixel Segmentation 10

An Ensemble Convolutional Neural Network for Recognizing and...

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10th International Conference on Information Systems and Computing technology, ISCTech 2022

作者： Yang, Fuqiang Choi, Yeonsung Yang, Fuping Xin, Ke Zhao, Xuechen School of Data and Computer Science Shandong Women's University Jinan China School of Computer Information and Communication Engineering Kunsan National University Kunsan Korea Republic of Shandong Labor Vocational and Technical College Network Center Jinan China College of Information Science and Engineering Binzhou University Binzhou China School of Computing National University of Defense Technology Changsha China

ISBN: (纸本)9798350332933

In the agricultural field., the problem of pests attacking crops has long plagued farmers. In order to detect and monitor the development of pests in time, compared with traditional methods, deep learning techniques that have gradually matured and become effective in recent years can be used. The success of deep learning technology in the field of computer vision in recent years can provide support for the solution of insect recognizes and counts in images under the complex background of agriculture. In recent studies, convolutional neural networks have improved the efficiency for the reorganization of insects in specific categories of crops. This paper suggested a method for recognizing and counting mango insects. In order to improve the accuracy and practicability of the model, this paper has done the following work: First of all, optimized and upgraded the A Large-Scale Benchmark Dataset for Insect Pest Recognition IP102 dataset version. Some of the pictures were cleaned, and unclear pictures that were difficult to extract in the early insects were deleted. Secondly, this paper suggested the EnRDNet algorithm to ensemble the ResNet and DenseNet models that have outstanding performance in the field of insect recognition. Third, this paper proposes to add a layer of confidence to the trained model when used in actual scene prediction which named Smart-EnRDNet algorithm to determine whether there are insect objects in the picture, and then output the insect category if there is. Experiment shows the efficiency of the proposed algorithms. © 2022 IEEE.

关键词： Convolutional neural networks

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CHECKWHY: Causal Fact Verification via Argument Structure

arXiv

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arXiv 2024年

作者： Si, Jiasheng Zhao, Yibo Zhu, Yingjie Zhu, Haiyang Lu, Wenpeng Zhou, Deyu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences China School of Computer Science and Engineering Southeast University China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science China

With the growing complexity of fact verification tasks, the concern with "thoughtful" reasoning capabilities is increasing. However, recent fact verification benchmarks mainly focus on checking a narrow scope of semantic factoids within claims and lack an explicit logical reasoning process. In this paper, we introduce CHECKWHY, a challenging dataset tailored to a novel causal fact verification task: checking the truthfulness of the causal relation within claims through rigorous reasoning steps. CHECKWHY consists of over 19K "why" claim-evidence-argument structure triplets with supports, refutes, and not enough info labels. Each argument structure is composed of connected evidence, representing the reasoning process that begins with foundational evidence and progresses toward claim establishment. Through extensive experiments on state-of-the-art models, we validate the importance of incorporating the argument structure for causal fact verification. Moreover, the automated and human evaluation of argument structure generation reveals the difficulty in producing satisfying argument structure by fine-tuned models or Chain-of-Thought prompted LLMs, leaving considerable room for future improvements1 Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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